NGS technologies for analyzing germplasm diversity in genebanks*.

BRIEFINGS IN FUNCTIONAL GENOMICS. VOL II. NO I. 38-50

doi:IO.I093/bfgp/elr046

NGS technologies for analyzing
germplasm diversity in gene banks

Benjamin Kilian and Andreas Graner

Advance Access publication date 17 January 2012

Abstract

More than 70 years after the first ex situ genebanks have been established, major efforts in this field are still con-
cerned with issues related to further completion of individual collections and securing of their storage. Attempts
regarding valorization of ex situ collections for plant breeders have been hampered by the limited availability of
phenotypic and genotypic information. With the advent of molecular marker technologies first efforts were made
to fingerprint genebank accessions, albeit on a very small scale and mostly based on inadequate DNA marker sys-
tems. Advances in DNA sequencing technology and the development of high-throughput systems for multiparallel
interrogation of thousands of single nucleotide polymorphisms (SNPs) now provide a suite of technological platforms
facilitating the analysis of several hundred of Gigabases per day using state-of-the-art sequencing technology or,
at the same time, of thousands of SNPs. The present review summarizes recent developments regarding the deploy-
ment of these technologies for the analysis of plant genetic resources, in order to identify patterns of genetic diver-
sity, map quantitative traits and mine novel alleles from the vast amount of genetic resources maintained in
genebanks around the world. It also refers to the various shortcomings and bottlenecks that need to be overcome
to leverage the full potential of high-throughput DNA analysis for the targeted utilization of plant genetic resources.

Keywords: genetic resources; next- generation sequencing; SNP; allele mining; genetic diversity; association analysis

INTRODUCTION

Plant breeding needs to focus on traits with the
greatest potential to increase yield under changing
climate conditions [1]. Agricultural practices have
gradually displaced local traditional varieties and
crop wild relatives, leading to a dramatic loss of in-
digenous biodiversity. Tapping into the rich genetic
diversity inherent in a crop species and their wild
relatives is a prerequisite for germplasm improvement
in the future [2-7; http://www.fao.org]. Hence,
new technologies must be developed to accelerate
breeding through improving genotyping and pheno-
typing methods and by accessing the available gen-
etic diversity stored in genebanks around the world.

Prior to the advent of molecular characterization,
accessions in germplasm collections were mainly
examined based on morphological characters and
phenotypic traits [8] . The development of molecu-
lar techniques now allows a more accurate analysis
of large collections. High-throughput (HT) technol-
ogies including DNA isolation, genotyping, pheno-
typing and next-generation sequencing (NGS)
provide new tools to add substantial value to gene-
bank collections. The integration of genomic
data into genebank documentation systems and its
combination with taxonomic, phenotypic and eco-
logical data will usher in a new era for the valoriza-
tion of plant genetic resources (PGR). From the

Corresponding author. Benjamin Kilian, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Genebank/
Genome Diversity, Corrensstrasse 3, 06466 Gatersleben, Germany. Tel.: +49 (0)39482 5-571; Fax: +49 (0)39482 5-500;
E-mail: kilian@ipk-gatersleben.de

*This article is dedicated to Heiko Parzies, plant geneticist and plant breeder who passed away far too early.

Benjamin Kilian is in the research group Genome Diversity at the IPK. His main interests are in genetic diversity, evolution and
domestication of Triticeae. He is in charge of projects aiming at exploiting natural genetic diversity by whole-genome association
mapping, high-throughput phenotyping and resequencing approaches.

Andreas Graner is managing director of the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) and the head of the
German Federal ex situ genebank for agricultural and horticultural plants. His research aims at developing genomics based approaches
for the valorization of plant genetics resources of barley (Hordeum vulgare) .

© The Author(s) 2012. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

NGS technologies for analyzing germplasm diversity in genebanks

39

Hordeum Accessions

Figure I: Ex situ collections are dominated by major crop species. (A) Of the more than 3000 crop species that are
maintained ex situ, 10 species totaling 3 540 000 accessions represent about half of the global inventory of ex situ re-
sources amounting to 7.4 million. (B) Correlation of the aggregated size of the ex situ collections the acreage fetched
by the individual crop species.

determination of phenotypic traits to the application
of NGS to whole genomes, every aspect of genom-
ics will have a great impact not only on PGR con-
servation, but also on their utilization in plant
breeding [9].

Identification and tracking of genetic variation has
become so efficient and precise that thousands of
candidate genes can be tracked within large gene-
bank collections [10]. Using NGS technologies, it is
possible to resequence candidate genes, entire tran-
scriptomes or entire plant genomes more efficiently
and economically than ever before. Advances in
sequencing technology will allow for whole-genome
resequencing of hundreds of individuals. In this way,
information on thousands of candidate genes and
candidate regions can be harnessed for thousands of
individuals to sample genetic diversity within and
between germplasm pools, to map Quantitative
Trait Loci (QTLs), to identify individual genes and
to determine their functional diversity. In this
review, we outline some important developments
in this field, where NGS technologies are expected
to enhance the value and thus the usefulness of gen-
ebank collections.

STATE OF EX SITU GERMPLASM
RESOURCES

PGR include cultivars, landraces, crop wild relatives
and mutants. The loss of genetic diversity in many

crop plants has resulted in efforts to collect PGR
which were initiated by Vavilov early in the 20th
century aiming at supporting plant breeders with
genetic material to extend genetic variability, as a
basis to create new crop varieties [11]. A wealth
of germplasm collections is available worldwide,
with more than 7 million accessions held in over
1.700 genebanks (http://www.fao.org/docrep/013/
il500e/il 500e00.htm). These do not evenly cover
all crop species but are highly biased regarding their
agricultural importance. About 50% of the global ex
situ germplasm is made up by only 10 crop species
with the three largest collections (wheat, rice and
barley) representing 28% of the global germplasm
(Figure 1). Passport and genotypic data suggest that
collections include different degrees of duplications
resulting in ^1.9—2.2 million distinct accessions
with the remaining being duplicates (http://www
.fao.org/docrep/013/il500e/il500e00.htm). Proper
conservation of PGR along with the development
of best genebank practices and pomoting the effect-
ive use is vital for food security in the future [12].
However, ex situ conservation is rather fragmented,
largely because it is mainly based on national pro-
grams and scattered institutional efforts. For instance,
barley (Hordeum vulgare L.), is maintained in more
than 200 collections worldwide amounting to ap-
proximately 470 000 accessions [13]. Other crop spe-
cies follow similar patterns [14]. Despite manifold
efforts to coordinate genebank activities conservation

40

Kilian and Graner

is still inefficient in many places and suffers from
variable or even lacking standards, unreliable access
and poor characterization and documentation of the
material [15]. Ex situ germplasm collections for crop
wild relatives are rather limited in size due to the
difficulties in maintaining non-domesticated plants
[16]. Introgression from wild to cultivated germ-
plasm and vice versa both during seed multiplication
in genebanks as well as in the wild pose a problem
for proper maintenance and correct classification of
the material, which usually is based on few morpho-
logical characters only. Another problem is that gen-
ebank accessions, even if they represent inbreeding
crop species, often are genetically heterogeneous and
may show residual heterozygosity. While this may
reflect the original genetic state, e.g. of a landrace
accession, it seriously can impair its molecular char-
acterization and its subsequent use for research and
breeding. Thus, most core collections are made up of
accessions which underwent purification by single
seed descent (SSD).

Systematic phenotypic analysis of genebank col-
lections is a time and resource intense effort which
has been mainly restricted to agronomic traits
that show a high heritability and can be assessed
based on the per se performance of an accession.
Therefore, most evaluation efforts were focused to
combine i.e. disease resistance and important mor-
phological characters (yield components) [8, 17].
Deep genetic and phenotypic characterization of
genetic resources by HT techniques, including rese-
quencing of enriched candidate genes and low-
coverage full-genome resequencing will increasingly
become available. Concomitantly large amounts of
data need to be integrated within the current docu-
mentation systems. Genebanks have to prepare for
entering the genomics era by developing new strate-
gies and novel information tools to assess the genetic
diversity represented in their collections. Although
there have been some successful examples of extract-
ing useful genes from genebanks, the vast potential of
this resource still remains largely untapped [18, 19].

CHARACTERIZATION OF
GERMPLASM BY MOLECULAR
MARKERS: THE CURRENT STATE

A large series of studies have been undertaken to
study diversity, domestication, evolution and phyl-
ogeny of PGR, largely selected from genebank col-
lections. Early studies considered morphological and

cytogenetic characters. Various other techniques and
molecular markers have been applied subsequently
[20—23]. Until recently, amplified fragment length
polymorphism (AFLP) or simple sequence repeats
(SSR) were the molecular markers of choice for
DNA fingerprinting of crop genomes [24—26].
Owing to their amenability to systematic develop-
ment and HT detection, SNP markers increasingly
applied to study genetic diversity in germplasm col-
lections of up to several hundreds of accessions.
Many of these collections have been established as
association panels for linkage disequilibrium (LD)
mapping, thus providing a first link between pheno-
typic and genotypic data sets. The corresponding
accessions have been selected from various germ-
plasm sources or breeding programs to represent a
rough cross section of the overall genetic diversity
available for a given species or for an ecogeographical
region [27, 28]. This is exemplified by a population
comprising 224 spring barley accessions, which were
selected from the Barley Core Collection, BCC [29]
and complemented by additional accessions to cover
the entire distribution range of this crop [30] . More
recently, aboutl500 spring barley landraces adapted
to temperate climate conditions were selected among
22 093 Hordeum accessions of the Federal ex situ gen-
ebank (IPK Gatersleben, Germany), based on their
origin and morphology. The whole set has been
genotyped by 43 SSR markers and analyzed for its
genetic structure. While this is intended to usher in
large-scale fingerprinting analysis of barley genebank
accessions, the approach still falls short of providing
informed molecular access to the entire collection.
Different marker systems for genetic diversity studies
and population parameters can be compared over a
collection as recently shown by [31] who compared
the performance of 42 SSR markers and 1536 SNP
markers. The marker type of choice and the number
of markers to be studied have to be adjusted for each
species and project.

Allele mining of individual loci

Plant accessions from wild or locally adapted landrace
genepools conserved in genebanks contain a rich
repertoire of alleles that have been left behind
by the selective processes of domestication, selection
and cross-breeding that paved the way to today's
elite cultivars. These resources stored in genebanks
remain underexplored owing to a lack of effi-
cient strategies to screen, isolate and transfer import-
ant alleles. The most effective strategy for

NGS technologies for analyzing germplasm diversity in genebanks

41

determining allelic richness at a given locus is cur-
rently to determine its DNA sequence in a represen-
tative collection of individuals. Large-scale allele
mining projects for germplasm collections at the mo-
lecular level are needed as the one described for Pm3
in wheat. Bhullar etal. [18] first selected a set of 1320
bread wheat landraces from a virtual collection of
16 089 accessions, using the focused identification
of germplasm strategy (FIGS) and isolated seven
new resistance alleles of the powdery mildew resist-
ance gene Pm3. Similarly, a series of novel alleles
have been detected for a recessive gene conferring
virus resistance in barley [32, 33]. Further resequen-
cing studies of candidate genes for agriculturally im-
portant traits have been published, however, from
smaller collections and mostly without functional
characterization [34—40].

Resequencing of candidate genes using Sanger
sequencing has been applied to study phylogenetic
relationships of crop plants, their domestication, evo-
lution, speciation and ecological adaptation. Early
studies resequenced a single locus or few loci in
only few individuals per species [41, 42]. Reduced
costs for Sanger sequencing using capillary instru-
ments and 9 6- well formats facilitated multilocus stu-
dies in larger collections [43—51].

NGS technologies to screen germplasm
collections

Large-scale NGS is now possible using platforms
such as Illumina/GA, Roche/GS FLX, Applied
Biosystems/SOLiD and cPAL sequencing [52, 53].
The declining cost of generating such data is trans-
forming all fields of genetics [54] . Many crop plant
genomes are characterized by the vast abundance of
repetitive DNA. For example, the genome of barley
comprises >5 Gb of DNA sequence of which <2%
can be accounted for by genes [55]. Therefore, to
avoid excessive sequencing of putatively non-in-
formative, repetitive DNAs, reduced-representation
sequencing techniques have been developed to
home in on subset of the genome for sequencing
[56, 57]. When combined with techniques for label-
ing reads (barcoding), DNA from many individuals
can be analyzed in the same pooled sequencing re-
action, and NGS provides an increasingly affordable
means. These technologies are therefore becoming a
standard choice for generating genetic data in fields
such as population genetics, conservation genetics
and molecular ecology. On the other hand, the
deluge of sequence data they will entail the necessity

to develop an appropriate IT infrastructure and new
computational solutions [58-64].

Sequencing many individuals at low depth is an-
other attractive strategy e.g. for complex trait associ-
ation studies as shown by [65]. While detailed
analysis of a single individual typically requires deep
sequencing, resequencing of many individuals allows
drastic reduction of sequencing depth when com-
bined with efficient genotype imputation to match
for missing data. Genotype imputation has been used
widely in the analysis of genome-wide association
studies (GWAS) to boost power and to facilitate
the combination of results across different studies
using meta-analyses [66, 67].

We have not yet reached the point at which rou-
tine whole-genome resequencing of large numbers
of crop plant genomes becomes feasible. Therefore,
it is necessary to select genomic regions of interest
and to enrich these regions before sequencing.
Sequencing targeted regions of DNA (e.g. the
exome or parts thereof) rather than complete gen-
omes will be likely the preferred approach for most
genomics applications including evolutionary biol-
ogy, association mapping and biodiversity conserva-
tion [68]. Sequencing targeted regions on massively
parallel-sequencing instruments requires methods for
concomitant enrichment of the templates to be
sequenced. There are several enrichment approaches
available, each with advantages and disadvantages
[69-72]. Resequencing allows fingerprinting of
many individuals without ascertainment bias which
is inherent to some SNP marker systems [73—75].

As outlined above, targeted resequencing of
hundreds of loci in genebank collections is already
feasible. Yet, the costs for DNA extraction, com-
plexity reduction and barcoding need to be brought
down for systematic resequencing of genebank col-
lections. In this context, large efforts have recently
been made to automate protocols for massively
parallel (re) sequencing and data analysis in order
to match the increasing instrument throughput.
These protocols that include e.g. large-scale auto-
matic library preparation and size selection on
robots [76] or fully automated construction of bar-
coded libraries [77] — might be useful paving the way
for automated NGS technologies to screen genebank
collections [78].

Multiparallel resequencing studies

Triggered by advancements in sequencing technol-
ogies, several crop genome sequences have been

42

Kilian and Graner

produced or are underway [79-82]. Once good
quality levels have been achieved, these sequences
will enable researchers to address all kinds of bio-
logical questions or to link sequence diversity accur-
ately to pheno types.

Rapid developments in NGS will soon make
whole-genome resequencing in several individuals
or targeted resequencing of large germplasm collec-
tions reality. This will help to eliminate an important
difficulty in the estimation of LD and genetic rela-
tionships between accessions obtained in bi-allelic
geno typing studies caused by ascertainment bias i.e.
the presence of rare alleles [73, 83—85].

Based on the available Arabidopsis thaliana (L.)
Heynh. genome sequence, Weigel and Mott [86]
advocated a 1001 Genomes project for Arabidopsis.
Several Arabidopsis lines have been sequenced since
[87, 88]. First studies on whole-genome resequen-
cing in crop species have been published for rice and
maize [66, 89, 90].

Combined genetic approaches for species, where
a complete genome sequence and millions of
SNPs are available, have been performed. Such
approaches that include e.g. large-scale geno typing,
targeted genomic enrichment, whole-genome rese-
quencing and GWAS have been addressed to iden-
tify allelic diversity, rare genetic variation, QTL and
their functional characterization [91-96] or to iden-
tify selective sweeps of favorable alleles and candidate
mutations that have had a prominent role in domes-
tication [97].

TRAIT MAPPING IN PLANTS
Genome-wide marker discovery using
NGS

SNPs are the most abundant form of genetic vari-
ation in eukaryotic genomes and are not a limiting
factor anymore, also not for crop species with large
genome sizes like barley [98]. SNP markers are rap-
idly replacing SSRs or Diversity Arrays Technology
(DArT) [99] markers because they are more abun-
dant, reproducible, amenable to automation and
increasingly cost-effective [100, 101]. SNP-based
resources are presently being developed and made
publicly available for broad application in crop re-
search [102].

A high-quality genomic sequence as it is available
for Arabidopsis and rice represents the ideal blueprint
for resequencing and the identification of SNPs.
But even for species with less complete genomic

sequences such as barley and wheat [103, 104] or
other species [105-109] NGS methods are valuable
for genome-wide marker development, genotyping
and targeted sequencing across the genomes of popu-
lations [110—112]. These new methods — which in-
clude e.g. reduced-representation libraries (RRLs)
[113—115], complexity reduction of polymorphic
sequences (CRoPS) [116, 117], restriction-site-
associated DNA sequencing (RAD-seq) [118] and
low-coverage sequencing for genotyping [119—121]
are applicable for genetic analysis to non-model spe-
cies, to species with high levels of repetitive DNA or
to breeding germplasm with low levels of poly-
morphism — without the need for prior sequence in-
formation. These methods can be applied to
compare SNP diversity within and between closely
related plant species or within wild natural popula-
tions [122, 123].

Genome-wide association studies in crop
plants

The systematic characterization and utilization of
naturally occurring genetic variation has become an
important approach in plant genome research and
plant breeding. So far, linkage mapping based on
bi-parental progenies has proven useful in detecting
major genes and QTLs [124, 125]. Although this
approach has been successful in many analyses, it
suffers from several drawbacks. LD or association
mapping is an attractive alternative to traditional
linkage mapping and has several advantages over
classical linkage mapping i.e. using unstructured
populations that have been subjected to many
recombination events [126-128]. GWAS in diverse
germplasm collections offer new perspectives
towards gene and allele discovery for traits of agri-
cultural importance and dissecting the genetic basis
of complex quantitative traits in plants [129, 130].
However, GWAS require a genome-wide assess-
ment of genetic diversity (preferably based on a ref-
erence genome sequence and resequenced parts
thereof), patterns of population structure, and the
decay of LD. For this, effective genotyping tech-
niques for plants, high-density marker maps, pheno-
typing resources, and if possible, a high-quality
reference genome sequence is required [131]. The
results of GWAS need in many cases confirmation
by linkage analysis.

GWAS have identified a large number of SNPs
associated with disease phenotypes in humans, also
in diverse worldwide populations [132]. Early

NGS technologies for analyzing germplasm diversity in genebanks

43

association mapping studies in crop plants were ham-
pered by the availability of a limited amount of
mapped markers and thus were mainly based on
resequencing candidate genes [39, 40]. The develop-
ment of comprehensive sets of SNP markers that can
be interrogated in highly multiparallel HT SNP gen-
otyping ushered in the era of germplasm diversity
studies and GWAS in crop plants. [87, 98, 119,
133-138].

For barley, few germplasm collections including
wild and landrace barley have been genotyped using
custom-made OPAs (oligo-pool assays) by Illumina
GoldenGate technology [139, 140]. SNP markers
significantly associated with traits are being used to
identify genomic regions that harbor candidate
genes for these traits in various collaborative barley
projects. It is relatively easy to detect marker- trait
associations in barley cultivar populations that
have extensive LD (5— lOcM). Conversely, popula-
tions with low LD are supposed to provide

high-resolution associations (landraces, <5 cM; wild
barley, <1 cM) but the number of markers needed to
find significant associations is relatively high. This
rapid decay in LD in populations of wild germplasm
is a key generic problem with genotyping for
bi-allelic SNPs. Furthermore, ascertainment bias of
bi-allelic SNP discovery i.e. caused by rare alleles and
alleles not present in the elite cultivars complicates
the situation in landraces and wild germplasm [73,
141]. Thus rare alleles are usually excluded from ana-
lysis. Higher marker coverage is required in order to
identify candidate genes more efficiently in diverse
collections. In case of barley, a high density SNP
Chip has been developed, which contains 7864
bi-allelic SNPs coming from NGS of a broad range
of barley cultivars (R. Waugh et al, unpublished
data). Such customized arrays for HT SNP genotyp-
ing can accelerate genetic gain in breeding programs.
First barley association panels have been genotyped
using this resource (Figure 2). Similar SNP chips are

0.1

Morex

Winter barleys

Figure 2: NeighborNet [I66] of Hamming distances for 6885 polymorphic SNPs among 27I barley cultivars using
the 9K Infinium iSELECT HD custom genotyping Bead Chip. Barley cultivars Barke, Bowman and Morex are high-
lighted as reference genotypes. Winter barleys form a cluster, which separates them clearly from the remaining
spring barley accessions.

44

Kilian and Graner

becoming available for an increasing number of crop
plants [142, 143]. Combined studies using GWA
mapping, comparative analysis, linkage mapping,
resequencing and functional characterization of can-
didate genes already enabled the identification of
candidate genes for selected traits [66, 91, 128].

While genotyping arrays are useful for assessing
population structure and the decay of LD across
large numbers of samples, low-coverage whole-
genome sequencing will become the genotyping
method of choice for GWAS in plant species [66] .
As for humans, GWAS for plants will become the
primary approach for identifying haplotypes and
genes with common alleles influencing complex
traits. However, common variations identified by
GWAS account for only a small fraction of trait her-
itability and are unlikely to explain the majority of
phenotypic variations of common traits. A potential
source of the missing heritability is the contribution
of rare alleles, insertion— deletion polymorphisms,
copy number variants and epigenetic differences —
that can be detected by NGS technologies. However,
testing the association of rare variants with pheno-
types of interest is challenging. Novel powerful asso-
ciation methods designed for large-scale resequencing
data have to be developed [144-149].

In the future, it can be expected that mapping
by sequencing will become the method of choice
to discover the genes underlying quantitative trait
variation in large purified germplasm collections
[150-152] or epigenetic variation [84, 88, 153-155].

OUTLOOK

PGR of crop wild relatives or locally adapted crop
landraces contain a rich repertoire of alleles that have
been lost by selective processes that generated our
today's elite cultivars. Such alleles represent an
invaluable asset to cope with future challenges for
sustainable agricultural development and food pro-
duction [156, 157]. In the medium run, draft
genome sequences will be available for all major
and many neglected crops species and resequencing
of these genomes in germplasm collections will yield
a wealth of information. Transforming this deluge of
data to information and knowledge will increase our
understanding in all fields of genetics including evo-
lution, ecology, domestication and breeding. Now is
a crucial time to explore the potential implications of
this information revolution for genebanks and to
recognize opportunities and limitations in applying

NGS tools and HT technologies to genebank col-
lections [56, 158].

Sequence informed conservation and
utilization of PGR

The availability of sequence information can make a
significant contribution to the conservation of PGR.
The high degree of redundancy found between dif-
ferent ex situ collections wastes a prohibitive amount
of resources (see above). Across the board, two-third
of the seed multiplication that is the most resource
intense step of all conservation efforts, could be made
redundant, if there were ways to unambiguously
identify duplicates. Most attempts to identify dupli-
cated samples suffered from the difficulty to agree on
a common set of markers for a given species, mani-
fold problems to reproduce DNA marker data be-
tween different labs. DNA sequences do not suffer
from such shortcomings and therefore represent an
ideal information platform to tackle the issue of re-
dundancy. Arguably, sequencing of ex situ collections
just for the sake of eliminating redundancy would be
too expensive an undertaking. Combination of this
effort with one of the issues mentioned below could
provide an added value.

Clearly large crop collections cannot be sequenced
in one draft. Against the backdrop of the evolving
technology, a stepwise approach should be envisaged.
Glaszmann et al [19] suggested the development of
'core reference sets' for our crops. A core reference
set (CRS) is to be understood as 'a set of genetic
stocks that are representative of the genetic resources
of the crop and are used by the scientific community
as a reference for an integrated characterization of its
biological diversity'. Every CRS will serve as a public,
standardized and well characterized resource for the
scientific community. Well characterized, multiplied,
isolated CRS have to be maintained for reference
purposes, comparative studies, future reanalysis and
integrative genomic analysis [59].

For this, already existing core collections must
be transformed into genetic stocks, purified (homo-
geneous/stabilized) and taxonomically classified to
facilitate practical choices for comparative associ-
ation studies. One other approach is to select di-
verse accessions directly from genebank collections
based on all available pre-existing characterization
and evaluation data (C&E), pedigree, origin and
collection site information. Survey genotyping to
test the purity of accessions can be done with vari-
ous molecular marker types such as inter-simple

NGS technologies for analyzing germplasm diversity in genebanks

45

Ex situ collections

Core Reference Sets/Core Collections

r

Genotyping/Sequencing

Phenotypic analysis

Trait mapping

Gene isolation
Allele mining

J

Plant Breeding

Figure 3: DNA genotyping and sequencing as integral components for conservation and valorization of plant gen-
etic resources.

sequence repeats (ISSRs) or AFLPs. Mixed acces-
sions including more than one genotype have to
be advanced by SSD before entering into system-
atic molecular and phenotypic characterization
(Figure 3).

The scope of a genebank may be extended to that
of a DNA bank, similar to biobanks devoted to target
medical research [159]. The various implications of
DNA banks for PGR have been discussed elsewhere.
Common standards and Biobank Information
Management Systems (BIMSs) have to be developed
to deal with highly complex and diverse sets of meta-
data. Advanced technologies for high-quality bio-
sample storage and management systems are
available and have to be implemented [160, 161].

Precise phenotyping is one of the major bottle-
necks in characterizing large collections. New,
non-invasive, automated image analysis technologies
are currently under development for systematic phe-
notyping under greenhouse and field conditions
using novel sensing and imaging technologies.
Phenomics is an emerging field, in which large and
complex data sets are being produced. These require
long-term storage for future reanalysis when software
tools and algorithms have improved or for compara-
tive analysis [162, 163]. Pre-selection of contrasting
accessions by different strategies including allele
mining approaches, genotyping using custom-made
Bead Chips and morphological characterization are
effective strategies to reduce the number of

accessions prior to thorough phenotyping, the
latter being the most time consuming step.

The ultimate goal regarding the valorization of
PGR will be the deployment of novel alleles that
will improve the trait under consideration. While
resequencing of candidate genes is a straightforward
approach to identify allelic variation, deployment of
novel alleles in a breeding program is contingent on
prior phenotypic validation. So far, this has been re-
stricted to major genes, e.g. for disease resistance and
seed quality. Validation of alleles of candidate genes
for quantitative traits still remains a major challenge
(i.e. Targeting Induced Local Lesions in Genomes
(TILLING)), [164, 165]. In this regard, the ability
to replace alleles by site specific recombination
could spur the targeted utilization of PGR and
thus greatly enhance the value chain of Biodiversity.

Key Points

• Novel statistical approaches and promising NGS approaches are
becoming available to screen major genebank collections. NGS
will provide a platform for the large-scale development of SNPs
that can be assayed in highly parallel manner for HT genotyping.

• Alternatively to SNP analysis genotyping by sequencing will be
employed to obtain information on SNP and haplotype patterns.

• A staggered strategy starting from core collections is proposed
to genotype and/or resequences genetic resources.

• Leverage of the full potential of sequence information on PGR
depends on the availability of accurate phenotypic information
and the potential to validate novel alleles at the phenotypic level.

46

Kilian and Graner

FUNDING

This work has been funded by Leibniz Institute of
Plant Genetics and Crop Plant Research (IPK).

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